Dynamic Product Ads (DPA) require retrieving relevant items from multi-million product catalogs, balancing two competing objectives: retargeting (re-surfacing known interests) and prospecting (discovering new categories). While Large Language Models (LLMs) capture semantic intent better than traditional embedding model...
Cong-Fei Zhang, J. Ma, Xiao-Dong Liu et al.· arXiv.org· 0 citations
Embedding-based retrieval is at the core of industrial recommender systems, but a single user embedding is often too limited to capture a user's diverse interests. Multi-interest retrieval addresses this by using multiple user embeddings, yet existing methods still suffer from two issues: interest collapse, where diffe...
Xiao-Dong Liu, Cong-Fei Zhang, Hsiang-Wei Chao et al.· 0 citations
EGR is proposed, an Embedding-Native Generative Retrieval framework that uses a single shared LLM to learn item representations from item metadata and user representations from interaction histories in one embedding space, simplifying system design while improving retrieval quality and ad performance.
